GEP algorithm for oil spill detection and differentiation from lookalikes in RISAT SAR images

Vanjare, A and Arvind, CS and Omkar, SN and Kishore, J and Kumar, V (2019) GEP algorithm for oil spill detection and differentiation from lookalikes in RISAT SAR images. In: 7th International Conference on Soft Computing for Problem Solving, SocProS 2017, 23 - 24 December 2017, Bhubaneswar, pp. 435-446.

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Abstract

Earth is covered with three fourth of water and one fourth of land. Ninety percent of world cargo transportation happens via ships that sail across great waters. Increase in sea traffic at the ports, natural disasters, technical, human errors may lead to oil spilling on oceanic surface. These spills will cause a lot of damage to marine ecosystem. Estimating the damage is one of the challenging tasks that can be addressed using remote sensing technology. In this paper, detection and differentiating look-alike image features of four different oceanic regions are studied using gene expression programming (GEP) algorithms on RISAT-1 SAR satellite images. GEP algorithm clearly differentiates lookalike image feature pixel from oil spill image feature pixel with classification accuracy on four different oil spill datasets is more than 98. Proving GEP can be used for two class oil spill detection and classification problem.

Item Type:	Conference Paper
Publication:	Advances in Intelligent Systems and Computing
Publisher:	Springer Verlag
Additional Information:	The copyright for this article belongs to Springer Verlag.
Keywords:	Classification (of information); Disasters; Ecosystems; Gene expression; Oil spills; Pixels; Remote sensing; Soft computing; Synthetic aperture radar, Cargo transportation; Classification accuracy; Gene expression programming; Image pixels; Natural disasters; Oil spill detection; Remote sensing technology; RISAT-1, Radar imaging
Department/Centre:	Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited:	15 Nov 2022 09:16
Last Modified:	15 Nov 2022 09:16
URI:	https://eprints.iisc.ac.in/id/eprint/78028

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